OpenRaiser/Intern-Atlas huggingface dataset:
On the Capacity of the Oversampled Wiener Phase Noise Channel
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, the capacity of the oversampled Wiener phase noise (OWPN) channel is investigated. The OWPN channel is a discrete-time point-to-point channel with a multi-sample receiver in which the channel output is affected by both additive and multiplicative noise. The additive noise is a white standard Gaussian process while the multiplicative noise is a Wiener phase noise process. This channel generalizes a number of channel models previously studied in the literature which investigate the effects of phase noise on the channel capacity, such as the Wiener phase noise channel and the non-coherent channel. We derive upper and inner bounds to the capacity of OWPN channel: (i) an upper bound is derived through the I-MMSE relationship by bounding the Fisher information when estimating a phase noise sample given the past channel outputs and phase noise realizations, then (ii) two inner bounds are shown: one relying on coherent combining of the oversampled channel outputs and one relying on non-coherent combining of the samples. After capacity, we study generalized degrees of freedom (GDoF) of the OWPN channel for the case in which the oversampling factor grows with the average transmit power $P$ as $P$? and the frequency noise variance as $P^α$?. Using our new capacity bounds, we derive the GDoF region in three regimes: regime (i) in which the GDoF region equals that of the classic additive white Gaussian noise (for $β\leq 1$), one (ii) in which GDoF region reduces to that of the non-coherent channel (for $β\geq \min \{α,1\}$) and, finally, one in which partially-coherent combining of the over-samples is asymptotically optimal (for $2 α-1\leq β\leq 1$). Overall, our results are the first to identify the regimes in which different oversampling strategies are asymptotically optimal.
Evaluation of a course mediatised with Xerte
Venue: arXiv cs.MM 2020
Year: 2020

Interactive multimedia educational content has recently been of interest to attract attention on the learner and increase understanding by the latter. In parallel several open source authoring tools offer a quick and easy production of this type of content. As such, our contribution is to mediatize a course i.e. 'English' with the authoring system 'Xerte' which is intended both for simple users and developers in ActionScript. An experiment of course is conducted on a sample of a private school's students. At the end of this experience, we administered a questionnaire to evaluate the device, the results obtained, evidenced by the favorable reception of interactive multimedia integration in educational content.
Self Organization Agent Oriented Dynamic Resource Allocation on Open Federated Clouds Environment
Venue: arXiv cs.DC 2020
Year: 2020

To ensure uninterrupted services to the cloud clients from federated cloud providers, it is important to guarantee an efficient allocation of the cloud resources to users to improve the rate of client satisfaction and the quality of the service provisions. It is better to get as more computing and storage resources as possible. In cloud domain several Multi Agent Resource Allocation methods have been proposed to implement the problem of dynamic resource allocation. However the problem is still open and many works to do in this field. In cloud computing robustness is important so in this paper we focus on auto-adaptive method to deal with changes of open federated cloud computing environment. Our approach is hybrid, we first adopt an existing organizations optimization approach for self organization in broker agent organization to combine it with already existing Multi Agent Resource Allocation approach on Federated Clouds. We consider an open clouds federation environment which is dynamic and in constant evolution, new cloud operators can join the federation or leave this one. At the same time our approach is multi criterion which can take in account various parameters (i.e. computing load balance of mediator agent, geographical distance (network delay) between costumer and provider...).
An IoT Platform-as-a-service for NFV Based -- Hybrid Cloud / Fog Systems
Venue: arXiv cs.DC 2020
Year: 2020

Cloud computing, despite its inherent advantages (e.g., resource efficiency) still faces several challenges. the wide are network used to connect the cloud to end-users could cause high latency, which may not be tolerable for some applications, especially Internet of Things (IoT applications. Fog computing can reduce this latency by extending the traditional cloud architecture to the edge of the network and by enabling the deployment of some application components on fog nodes. Application providers use Platform-as-a-Service (PaaS) to provision (i.e., develop, deploy, manage, and orchestrate) applications in cloud. However, existing PaaS solutions (including IoT PaaS) usually focus on cloud and do not enable provisioning of applications with components spanning cloud and fog. provisioning such applications require novel functions, such as application graph generation, that are absent from existing PaaS. Furthermore, several functions offered by existing PaaS (e.g., publication/discovery) need to be significantly extended in order to fit in a hybrid cloud/fog environment. In this paper, we propose a novel architecture for PaaS for hybrid cloud/fog system. It is IoT use case-driven, and its applications' components are implemented as Virtual Network Functions (VNFs) with execution sequences modeled s graphs with sub-structures such as selection and loops. It automates the provisioning of applications with components spanning cloud and fog. In addition, it enables the discovery of existing cloud and fog nodes and generates application graphs. A proof of concept is built based on Cloudify open source. Feasibility is demonstrated by evaluating its performance when PaaS modules and application components are placed in clouds and fogs in different geographical locations.
A Comprehensive Study on Temporal Modeling for Online Action Detection
Venue: arXiv cs.HC 2020
Year: 2020

Online action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years. A typical OAD system mainly consists of three modules: a frame-level feature extractor which is usually based on pre-trained deep Convolutional Neural Networks (CNNs), a temporal modeling module, and an action classifier. Among them, the temporal modeling module is crucial which aggregates discriminative information from historical and current features. Though many temporal modeling methods have been developed for OAD and other topics, their effects are lack of investigation on OAD fairly. This paper aims to provide a comprehensive study on temporal modeling for OAD including four meta types of temporal modeling methods, \ie temporal pooling, temporal convolution, recurrent neural networks, and temporal attention, and uncover some good practices to produce a state-of-the-art OAD system. Many of them are explored in OAD for the first time, and extensively evaluated with various hyper parameters. Furthermore, based on our comprehensive study, we present several hybrid temporal modeling methods, which outperform the recent state-of-the-art methods with sizable margins on THUMOS-14 and TVSeries.
Engineering AI Systems: A Research Agenda
Venue: arXiv cs.LG 2020
Year: 2020

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this paper, we provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that we have studied. The main contribution of the paper is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.
Exploring an Application of Virtual Reality for Early Detection of Dementia
Venue: arXiv cs.HC 2020
Year: 2020

Facing the severe global dementia problem, an exploration was conducted adopting the technology of virtual reality (VR). This report lays a technical foundation for further research project "Early Detection of Dementia Using Testing Tools in VR Environment", which illustrates the process of developing a VR application using Unity 3D software on Oculus Go. This preliminary exploration is composed of three steps, including 3D virtual scene construction, VR interaction design and monitoring. The exploration was recorded to provide basic technical guidance and detailed method for subsequent research.
Channels' Confirmation and Predictions' Confirmation: from the Medical Test to the Raven Paradox
Venue: arXiv cs.AI 2020
Year: 2020

After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple discovered the Raven Paradox (RP). Then, Carnap used the logical probability increment as the confirmation measure. So far, many confirmation measures have been proposed. Measure F among them proposed by Kemeny and Oppenheim possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al., and normalizing property suggested by many researchers. Based on the semantic information theory, a measure b* similar to F is derived from the medical test. Like the likelihood ratio, b* and F can only indicate the quality of channels or the testing means instead of the quality of probability predictions. And, it is still not easy to use b*, F, or another measure to clarify the RP. For this reason, measure c* similar to the correct rate is derived. The c* has the simple form: (a-c)/max(a, c); it supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the RP. Some examples are provided to show why it is difficult to use one of popular confirmation measures to eliminate the RP. Measure F, b*, and c* indicate that fewer counterexamples' existence is more essential than more positive examples' existence, and hence, are compatible with Popper's falsification thought.
Resilient Collaborative Privacy for Location-Based Services
Venue: arXiv cs.CR 2020
Year: 2020

Location-based Services (LBSs) provide valuable services, with convenient features for users. However, the information disclosed through each request harms user privacy. This is a concern particularly with honest-but-curious LBS servers, which could, by collecting requests, track users and infer additional sensitive user data. This is the motivation of both centralized and decentralized location privacy protection schemes for LBSs: anonymizing and obfuscating LBS queries to not disclose exact information, while still getting useful responses. Decentralized schemes overcome the disadvantages of centralized schemes, eliminating anonymizers and enhancing users' control over sensitive information. However, an insecure decentralized system could pose even more serious security threats than privacy leakage. We address exactly this problem, by proposing security enhancements for mobile data sharing systems. We protect user privacy while preserving accountability of user activities, leveraging pseudonymous authentication with mainstream cryptography. Our design leverages architectures proposed for large scale mobile systems, while it incurs minimal changes to LBS servers as it can be deployed in parallel to the LBS servers. This further motivates the adoption of our design, in order to cater to the needs of privacy-sensitive users. We provide an analysis of security and privacy concerns and countermeasures, as well as a performance evaluation of basic protocol operations showing the practicality of our design.
batchboost: regularization for stabilizing training with resistance to underfitting & overfitting
Venue: arXiv cs.LG 2020
Year: 2020

Overfitting & underfitting and stable training are an important challenges in machine learning. Current approaches for these issues are mixup, SamplePairing and BC learning. In our work, we state the hypothesis that mixing many images together can be more effective than just two. Batchboost pipeline has three stages: (a) pairing: method of selecting two samples. (b) mixing: how to create a new one from two samples. (c) feeding: combining mixed samples with new ones from dataset into batch (with ratio $γ$). Note that sample that appears in our batch propagates with subsequent iterations with less and less importance until the end of training. Pairing stage calculates the error per sample, sorts the samples and pairs with strategy: hardest with easiest one, than mixing stage merges two samples using mixup, $x_1 + (1-λ)x_2$. Finally, feeding stage combines new samples with mixed by ratio 1:1. Batchboost has 0.5-3% better accuracy than the current state-of-the-art mixup regularization on CIFAR-10 & Fashion-MNIST. Our method is slightly better than SamplePairing technique on small datasets (up to 5%). Batchboost provides stable training on not tuned parameters (like weight decay), thus its a good method to test performance of different architectures. Source code is at: https://github.com/maciejczyzewski/batchboost
Bipartite Perfect Matching as a Real Polynomial
Venue: arXiv cs.DM 2020
Year: 2020

We obtain a description of the Bipartite Perfect Matching decision problem as a multilinear polynomial over the Reals. We show that it has full degree and $(1-o_n(1))\cdot 2^{n^2}$ monomials with non-zero coefficients. In contrast, we show that in the dual representation (switching the roles of 0 and 1) the number of monomials is only exponential in $Θ(n \log n)$. Our proof relies heavily on the fact that the lattice of graphs which are "matching-covered" is Eulerian.
LOcAl DEcisions on Replicated States (LOADER) in programmable data planes: programming abstraction and experimental evaluation
Venue: arXiv cs.NI 2020
Year: 2020

Programmable data planes recently emerged as a prominent innovation in Software Defined Networking (SDN), by permitting support of stateful flow processing functions over hardware network switches specifically designed for network processing. Unlike early SDN solutions such as OpenFlow, modern stateful data planes permit to keep (and dynamically update) local per-flow states inside network switches, thus dramatically improving reactiveness of network applications to state changes. Still, also in stateful data planes, the control and update of non-local states is assumed to be completely delegated to a centralized controller and thus accessed only at the price of extra delay. Our LOADER proposal aims at contrasting the apparent dichotomy between local states and global states. We do so by introducing a new possibility: permit to take localized (in-switch) decisions not only on local states but also on replicated global states, thus providing support for network-wide applications without incurring the drawbacks of classical approaches. To this purpose, i) we provide high-level programming abstractions devised to define the states and the update logic of a generic network-wide application, and ii) we detail the underlying low level state management and replication mechanisms. We then show LOADER's independence of the stateful data plane technology employed, by implementing it over two distinct stateful data planes (P4 switches and OPP - Open Packet Processor - switches), and by experimentally validating both implementations in an emulated testbed using a simple distributed Deny-of-Service (DoS) detection application.
Properties of the Tangle for Uniform Random and Random Walk Tip Selection
Venue: arXiv cs.DC 2020
Year: 2020

The growing number of applications for distributed ledger technologies is driving both industry and academia to solve the limitations of blockchain, particularly its scalability issues. Recent distributed ledger technologies have replaced the blockchain linear structure with a more flexible directed acyclic graph in an attempt to accommodate a higher throughput. Despite the fast-growing diffusion of directed acyclic graph based distributed ledger technologies, researchers lack a basic understanding of their behavior. In this paper we analyze the Tangle, a directed acyclic graph that is used (with certain modifications) in various protocols such as IOTA, Byteball, Avalanche or SPECTRE. Our contribution is threefold. First, we run simulations in a continuous-time model to examine tip count stability and cumulative weight evolution while varying the rate of incoming transactions. In particular we confirm analytical predictions on the number of tips with uniform random tip selection strategy. Second, we show how different tip selection algorithms affect the growth of the Tangle. Moreover, we explain these differences by analyzing the spread of exit probabilities of random walks. Our findings confirm analytically derived predictions and provide novel insights on the different phases of growth of cumulative weight as well as on the average time difference for a transaction to receive its first approval when using distinct tip selection algorithms. Lastly, we analyze simulation overhead and performance as a function of Tangle size and compare results for different tip selection algorithms.
EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions
Venue: arXiv cs.CV 2020
Year: 2020

The EmoPain 2020 Challenge is the first international competition aimed at creating a uniform platform for the comparison of machine learning and multimedia processing methods of automatic chronic pain assessment from human expressive behaviour, and also the identification of pain-related behaviours. The objective of the challenge is to promote research in the development of assistive technologies that help improve the quality of life for people with chronic pain via real-time monitoring and feedback to help manage their condition and remain physically active. The challenge also aims to encourage the use of the relatively underutilised, albeit vital bodily expression signals for automatic pain and pain-related emotion recognition. This paper presents a description of the challenge, competition guidelines, bench-marking dataset, and the baseline systems' architecture and performance on the three sub-tasks: pain estimation from facial expressions, pain recognition from multimodal movement, and protective movement behaviour detection.
A Comprehensive Study of Bloated Dependencies in the Maven Ecosystem
Venue: arXiv cs.SE 2020
Year: 2020

Build automation tools and package managers have a profound influence on software development. They facilitate the reuse of third-party libraries, support a clear separation between the application's code and its external dependencies, and automate several software development tasks. However, the wide adoption of these tools introduces new challenges related to dependency management. In this paper, we propose an original study of one such challenge: the emergence of bloated dependencies. Bloated dependencies are libraries that the build tool packages with the application's compiled code but that are actually not necessary to build and run the application. This phenomenon artificially grows the size of the built binary and increases maintenance effort. We propose a tool, called DepClean, to analyze the presence of bloated dependencies in Maven artifacts. We analyze 9,639 Java artifacts hosted on Maven Central, which include a total of 723,444 dependency relationships. Our key result is that 75.1% of the analyzed dependency relationships are bloated. In other words, it is feasible to reduce the number of dependencies of Maven artifacts up to 1/4 of its current count. We also perform a qualitative study with 30 notable open-source projects. Our results indicate that developers pay attention to their dependencies and are willing to remove bloated dependencies: 18/21 answered pull requests were accepted and merged by developers, removing 131 dependencies in total.
Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework
Venue: arXiv cs.CV 2020
Year: 2020

We propose a hybrid method for stereo disparity estimation by combining block and region-based stereo matching approaches. It generates dense depth maps from disparity measurements of only 18 % image pixels (left or right). The methodology involves segmenting pixel lightness values using fast K-Means implementation, refining segment boundaries using morphological filtering and connected components analysis; then determining boundaries' disparities using sum of absolute differences (SAD) cost function. Complete disparity maps are reconstructed from boundaries' disparities. We consider an application of our method for depth-based selective blurring of non-interest regions of stereo images, using Gaussian blur to de-focus users' non-interest regions. Experiments on Middlebury dataset demonstrate that our method outperforms traditional disparity estimation approaches using SAD and normalized cross correlation by up to 33.6 % and some recent methods by up to 6.1 %. Further, our method is highly parallelizable using CPU and GPU framework based on Java Thread Pool and APARAPI with speed-up of 5.8 for 250 stereo video frames (4,096 x 2,304).
Performance-Driven Internet Path Selection
Venue: arXiv cs.NI 2020
Year: 2020

Internet routing can often be sub-optimal, with the chosen routes providing worse performance than other available policy-compliant routes. This stems from the lack of visibility into route performance at the network layer. While this is an old problem, we argue that recent advances in programmable hardware finally open up the possibility of performance-aware routing in a deployable, BGP-compatible manner. We introduce ROUTESCOUT, a hybrid hardware/software system supporting performance-based routing at ISP scale. In the data plane, ROUTESCOUT leverages P4-enabled hardware to monitor performance across policy-compliant route choices for each destination, at line-rate and with a small memory footprint. ROUTESCOUT's control plane then asynchronously pulls aggregated performance metrics to synthesize a performance-aware forwarding policy. We show that ROUTESCOUT can monitor performance across most of an ISP's traffic, using only 4 MB of memory. Further, its control can flexibly satisfy a variety of operator objectives, with sub-second operating times.
On the Performance of Quickest Detection Spectrum Sensing: The Case of Cumulative Sum
Venue: arXiv cs.IT 2020
Year: 2020

Quickest change detection (QCD) is a fundamental problem in many applications. Given a sequence of measurements that exhibits two different distributions around a certain flipping point, the goal is to detect the change in distribution around the flipping point as quickly as possible. The QCD problem appears in many practical applications, e.g., quality control, power system line outage detection, spectrum reuse, and resource allocation and scheduling. In this paper, we focus on spectrum sensing as our application since it is a critical process for proper functionality of cognitive radio networks. Relying on the cumulative sum (CUSUM), we derive the probability of detection and the probability of false alarm of CUSUM based spectrum sensing. We show the correctness of our derivations using numerical simulations.